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BackgroundHigh-throughput screening (HTS) programs are increasingly adopting high-content technologies that can better inform the selection of drug candidates early on in the pipelines. For cancer immunotherapy, proteomics tools to investigate interactions between cancer and immune cells compromise either content or cost, limiting access to phenotypic data. The affordable gold-standard in proteomics, the ELISA, has proven difficult to scale. At fault has been the cross-reactivity between ELISA reagents when multiplexing beyond a few dozen antibody pairs. Here, we describe the nELISA: a massively-parallelized high-throughput miniaturized ELISA with a content, cost and throughput amenable to HTS, and demonstrate its applicability to characterize immune phenotypes in co-culture systems.MethodsTo overcome the long-standing cross-reactivity issue, the nELISA uses DNA oligos to pre-assemble each pair of antibodies onto a spectrally barcoded microparticle set. The resulting reagents are fully-integrated nELISA sensors that can be read-out on commercial cytometers, enabling highly-multiplexed and high-throughput analysis. Using this approach, we developed a comprehensive inflammatory panel containing 191 cytokines, chemokines, proteases, growth factors, and soluble receptors. Our results show that the nELISA can maintain single-plex specificity, sensitivity, and quantification as content is scaled to 191-plex. Furthermore, the nELISA performs at a throughput of 1536 samples/cytometer/day, yielding >300,000 data points in a single day, at a cost amenable to high-throughput screening.ResultsTo demonstrate the nELISA’s utility in HTS, we ran the largest PBMC secretome screen to date, in which >7000 PBMC samples were treated with various inflammatory stimuli, and further perturbed with a selected library of 80 recombinant protein ‘perturbagens’. 191 secreted proteins were profiled in all samples, resulting in ~1.4M datapoints (figure 1A). The nELISA profiles were able to capture phenotypes associated with specific stimulation conditions, individual donors, and potent cytokine perturbagens. By compensating for stimulation and donor differences, we clustered perturbagens according to their effects on PBMC secretomes, identifying well-established cell responses such as Th1 or Th2. Novel phenotypic effects were also identified, such as distinct responses to the near identical CXCL12 alpha and beta isoforms (figure 1B). Interestingly, we observed important similarities between PBMC responses to the cytokine drugs IFN beta and IL-1 Receptor antagonist, supporting the use of anakinra as a replacement for IFN beta in certain indications.ConclusionsThe nELISA captures broad secretome ranges and subtle differences in immune phenotypes, revealing critical insights in cell-based screens. Thus, the nELISA is a powerful new tool for cancer immunotherapy assays, including phenotypic screening, target identification/deconvolution, and discovery of markers of target engagement.Abstract 61 Figure 1High-throughput screen of PBMC responses demonstrates the use of the nELISA for drug discovery. (A) Screen design: PBMCs isolated from six donors were treated with inflammatory stimuli at indicated concentrations, and further perturbed with 80 recombinant cytokine \"perturbagens\", generating a total of 7,392 samples; after 24 hours, concentrations of 191 secreted proteins were measured in the supernatant of each sample using the nELISA. (B) UMAP dimensionality reduction of the entire nELISA dataset; datapoints are colored (from left supernatant of each sample using the nELISA. (B) UMAP dimensionality reduction of the entire nELISA dataset; datapoints are colored (from left to right by stimulation condition, by donor, by stimulation concentration, or by individual cytokind perturbagens with strong effects, as indicated.

Cell navigation is directed by inhomogeneous distributions of extracellular cues. It is well known that noise plays a key role in biology and is present in naturally occurring gradients at the micro- and nanoscale, yet it has not been studied with gradients in vitro. Here, we introduce novel algorithms to produce ordered and random gradients of discrete nanodots--called digital nanodot gradients (DNGs)--according to monotonic and non-monotonic density functions. The algorithms generate continuous DNGs, with dot spacing changing in two dimensions along the gradient direction according to arbitrary mathematical functions, with densities ranging from 0.02% to 44.44%. The random gradient algorithm compensates for random nanodot overlap, and the randomness and spatial homogeneity of the DNGs were confirmed with Ripley's K function. An array of 100 DNGs, each 400×400 µm2, comprising a total of 57 million 200×200 nm2 dots was designed and patterned into silicon using electron-beam lithography, then patterned as fluorescently labeled IgGs on glass using lift-off nanocontact printing. DNGs will facilitate the study of the effects of noise and randomness at the micro- and nanoscales on cell migration and growth.

BackgroundHigh-plex proteomics is critical to enabling cancer research through comprehensive profiling of immune and tumor-derived signals, facilitating early detection, biomarker discovery, immune-related adverse event (irAE) prediction, and real-time monitoring of therapeutic responses. To date, proteomics has been constrained by flexibility, high costs, and inconsistent quantification. Here, we present Omni 1000, a quantitative 1000-plex solution powered by nELISA technology; designed for broad, reproducible, and cost-effective protein measurement. Omni 1000 delivers 0.1 pg/mL sensitivity, 99.99% specificity, and dynamic range spanning 3-6 logs.MethodsOmni 1000 content was developed through rigorous, data-driven strategy to achieve comprehensive proteome-wide coverage while retaining high-value markers. The foundation is built from two sources: (1) a curated set of Most Valuable Proteins (MVPs)—biomarkers selected heuristically based on prevalence in key signaling pathways, translational research, and validated endpoints; and (2) large-scale, high-plex proteomic datasets with disease-association. To optimize, we iteratively applied Minimum Redundancy Maximum Relevance (mRMR) to reduce overlap, reconstruction loss minimization to preserve signal from high-dimensional datasets, and prioritized MVPs. Each iteration was validated against key biological ontologies—achieving 92% MVP coverage, 100% Reactome level 0, >80% Reactome level 1, 100% pharma-relevant KEGG signaling pathways, and 100% MeSH disease classes. In addition, we determined the disease prediction power of Omni 1000 content at >95% equivalent to a 3000+ panel, evaluated on the UK Biobank cohort.ResultsFor biomarker discovery, Omni 1000 makes large-scale and clinically relevant studies achievable through rapid readout with flow cytometry. We leveraged Omni 1000’s capabilities in a high-thoughput drug screening platform structured on patient-derived tumor organoids. Use of Omni 1000 demonstrated insights on baseline donor heterogeneity and drug compound responses and resistances specific to patient tissue profiles. Of interest for immunotherapy applications were compounds inducing cell death while promoting pro-inflammatory immune environments. We observed cytotoxicity with 2 CDK9 inhibitors in organoids across donors, through increased levels of intracellular proteins in culture supernatant and broad decreases in most other protein levels. They simultaneously resulted in increased secretion of chemokines CXCL2, CXCL3, CXCL5, and maintained CCL2 and IL-8 expression, possibly promoting additional immune involvement parallel to direct cell killing. These findings underscore Omni 1000’s capacity to profile functional heterogeneity in tumor immune microenvironments and support development of precision medicine with immunotherapeutic potential.ConclusionsTogether, we demonstrate a novel 1000-plex solution, Omni 1000, with content balancing critical targets and biological breadth, and demonstrated real-world utility in early detection of disease and high throughput cancer drug development.

BackgroundProteomics holds great promise for cancer immunotherapy, with intensive efforts being exerted for early disease identification, patients selection, and adverse event prediction. Despite this potential, the high cost and low throughput of existing tools to profile circulating proteins render such studies prohibitively slow and costly, limiting their wide-spread application. As a result, proteomics studies in the field have been constrained to sample sizes in the 10s and 100s , restricting the power to discover key biomarkers. Here, we leverage a novel proteomics tool, the nELISA, to quantify ~600 circulating proteins across ~3000 samples from the RADIOHEAD (Resistance Drivers for Immuno-Oncology Patients Interrogated by Harmonized Molecular Datasets) cohort, a prospective study of 1070 immunotherapy naive pan-tumor patients on standard of care immune checkpoint inhibitor (ICI) therapy regimens from community oncology clinics.MethodsThe Nomic platform is a highly multiplexed immunoassay technology that enables the profiling of hundreds of proteins across 1536 samples per instrument daily, at significantly reduced costs. The method miniaturizes sandwich immunoassays by placing antibody pairs on the surface of color-coded microparticles, which can then be analyzed via high-throughput flow cytometry.ResultsUsing this technology, our preliminary data identified several markers of response to treatment; for example, PD-1 inhibitors result in increased circulating levels of soluble PD-1, and ICIs increase levels of several chemokines including CXCL9 and CXCL10, as seen in several other small-scale studies. We also identified several markers potentially predicting response to treatment and irAEs, which will require much larger datasets for validation. The scalable nature of the nELISA platform now allows us to validate these findings in a large longitudinal cohort, providing the power needed for such a broad biomarker discovery effort. In this presentation, we share the results of applying nELISA to the RADIOHEAD blood serum samples from pretreatment, early on-treatment, 6-month, and 12-month timepoints. For participants who experienced immune-related adverse events, additional samples were collected upon presentation and in follow-up visits - these samples were also analyzed in this study.ConclusionsPairing nELISA protein profiling of these longitudinal samples with associated demographic metadata and clinical outcomes provides an opportunity to identify clinically actionable mechanisms for ICI resistance and adverse events, discover targets for combination therapies and post-ICI treatment, and inform system biology approaches to elucidate disease pathways. Here, we highlight biomarkers and protein signatures related to patient outcomes, to reveal additional insights and further accelerate research in the field of cancer immunotherapy.

Cell navigation is directed by inhomogeneous distributions of extracellular cues. It is well known that noise plays a key role in biology and is present in naturally occurring gradients at the micro- and nanoscale, yet it has not been studied with gradients in vitro. Here, we introduce novel algorithms to produce ordered and random gradients of discrete nanodots called digital nanodot gradients (DNGs) according to monotonic and non-monotonic density functions. The algorithms generate continuous DNGs, with dot spacing changing in two dimensions along the gradient direction according to arbitrary mathematical functions, with densities ranging from 0.02% to 44.44%. The random gradient algorithm compensates for random nanodot overlap, and the randomness and spatial homogeneity of the DNGs were confirmed with Ripley s K function. An array of 100 DNGs, each 400 400 m2, comprising a total of 57 million 200 200 nm2 dots was designed and patterned into silicon using electron-beam lithography, then patterned as fluorescently labeled IgGs on glass using lift-off nanocontact printing. DNGs will facilitate the study of the effects of noise and randomness at the micro- and nanoscales on cell migration and growth.

We present the nELISA, a high-throughput, high-fidelity, and high-plex protein profiling platform. DNA oligonucleotides are used to pre-assemble antibody pairs on spectrally encoded microparticles and perform displacement-mediated detection. Spatial separation between non-cognate antibodies prevents the rise of reagent-driven cross-reactivity, while read-out is performed cost-efficiently and at high-throughput using flow cytometry. nELISA can measure both protein concentration and their post-translational modifications. We assembled an inflammatory panel of 191 targets that were multiplexed without cross-reactivity nor impact on performance vs 1-plex signals, with sensitivities as low as 0.1 pg/mL and measurements spanning 7 orders of magnitude. We then performed a large-scale inflammatory-secretome perturbation screen of peripheral blood mononuclear cells (PBMCs), with cytokines as both perturbagens and readouts, measuring 7,392 samples and generating ~1.4M protein data points in under a week; a significant advance in throughput compared to other highly multiplexed immunoassays. We uncovered 447 significant cytokine responses, including multiple putatively novel ones, that were conserved across donors and stimulation conditions. We validate nELISA for phenotypic screening, where its capacity to faithfully report hundreds of proteins make it a powerful tool across multiple stages of drug discovery.

We present the nELISA, a high-throughput, high-fidelity, and high-plex protein profiling platform. DNA oligonucleotides are used to pre-assemble antibody pairs on spectrally encoded microparticles and perform displacement-mediated detection. Spatial separation between non-cognate antibodies prevents the rise of reagent-driven cross-reactivity, while read-out is performed cost-efficiently and at high-throughput using flow cytometry. We assembled an inflammatory panel of 191 targets that were multiplexed without cross-reactivity or impact on performance vs 1-plex signals, with sensitivities as low as 0.1pg/mL and measurements spanning 7 orders of magnitude. We then performed a large-scale secretome perturbation screen of peripheral blood mononuclear cells (PBMCs), with cytokines as both perturbagens and read-outs, measuring 7,392 samples and generating ~1.5M protein datapoints in under a week, a significant advance in throughput compared to other highly multiplexed immunoassays. We uncovered 447 significant cytokine responses, including multiple putatively novel ones, that were conserved across donors and stimulation conditions. We also validated the nELISA's use in phenotypic screening, and propose its application to drug discovery.We present the nELISA, a high-throughput, high-fidelity, and high-plex protein profiling platform. DNA oligonucleotides are used to pre-assemble antibody pairs on spectrally encoded microparticles and perform displacement-mediated detection. Spatial separation between non-cognate antibodies prevents the rise of reagent-driven cross-reactivity, while read-out is performed cost-efficiently and at high-throughput using flow cytometry. We assembled an inflammatory panel of 191 targets that were multiplexed without cross-reactivity or impact on performance vs 1-plex signals, with sensitivities as low as 0.1pg/mL and measurements spanning 7 orders of magnitude. We then performed a large-scale secretome perturbation screen of peripheral blood mononuclear cells (PBMCs), with cytokines as both perturbagens and read-outs, measuring 7,392 samples and generating ~1.5M protein datapoints in under a week, a significant advance in throughput compared to other highly multiplexed immunoassays. We uncovered 447 significant cytokine responses, including multiple putatively novel ones, that were conserved across donors and stimulation conditions. We also validated the nELISA's use in phenotypic screening, and propose its application to drug discovery.

Pyroptosis represents one type of Programmed Cell Death (PCD). It is a form of inflammatory cell death that is canonically defined by caspase-1 cleavage and Gasdermin-mediated membrane pore formation. Caspase-1 initiates the inflammatory response (through IL-1β processing), and the N-terminal cleaved fragment of Gasdermin D polymerizes at the cell periphery forming pores to secrete pro-inflammatory markers. Cell morphology also changes in pyroptosis, with nuclear condensation and membrane rupture. However, recent research challenges canon, revealing a more complex secretome and morphological response in pyroptosis, including overlapping molecular characterization with other forms of cell death, such as apoptosis. Here, we take a multimodal, systems biology approach to characterize pyroptosis. We treated human Peripheral Blood Mononuclear Cells (PBMCs) with 36 different combinations of stimuli to induce pyroptosis or apoptosis. We applied both secretome profiling (nELISA) and high-content fluorescence microscopy (Cell Painting). To differentiate apoptotic, pyroptotic and control cells, we used canonical secretome markers and modified our Cell Painting assay to mark the N-terminus of Gasdermin-D. We trained hundreds of machine learning (ML) models to reveal intricate morphology signatures of pyroptosis that implicate changes across many different organelles and predict levels of many pro-inflammatory markers. Overall, our analysis provides a detailed map of pyroptosis which includes overlapping and distinct connections with apoptosis revealed through a mechanistic link between cell morphology and cell secretome.